MRI has proven to be a revolutionary diagnostic radiological tool, due to its high spatial resolution and excellent discrimination of soft tissues. MRI provides rich information about anatomical structure, enabling quantitative anatomical studies of diseases, the derivation of computerized anatomical atlases, as well as three-dimensional visualization of internal anatomy, for use in pre- and intra-operative visualization, and in the guidance of therapeutic intervention.
Applications that use the structural contents of MRI are facilitated by segmenting the imaged volume into tissue types. Such tissue segmentation is often achieved by applying statistical classification methods to the signal intensities, in conjunction with morphological image processing operations.
Conventional intensity-based classification of MR images has proven problematic, even when advanced techniques such as non-parametric multi-channel methods are used. Accommodating the intra-scan spatial intensity inhomogeneities that are due to the equipment has proven to be a persistent difficulty.
A conventional MRI system is generally depicted in FIG. 1. The tissue sample is inserted into a constant homogeneous magnetic field produced by main coil 4 of a main magnet 2. To enable spatial encoding of the image, the main magnetic field is varied in strength by the application of gradient magnetic fields produced by a bank of gradient coils 8, which are driven by selectively activated gradient amplifiers 6. The gradient fields make the frequencies and phase at which nuclear magnetic resonance (NMR) occurs vary with position. NMR in the tissue is excited by a magnetic field induced by transmitting coil 12, which is driven by RF electronics module 10. When the transmitting coil 12 is switched off, the tissue radiates RF signals which induce current in receiving coil 14. The RF electronics module 10 incorporates circuitry for detecting the currents induced in receiving coil 14.
Driving of the gradient coils 8 and the transmitting coil 12 is controlled by the central processing unit 20 via an input/output module 18. The measurement data is processed by CPU 20 and stored in memory 22. Optionally, the CPU 20 is connected to an arithmetic co-processor 24.
The MRI signal is derived from the RF signals emanating from the scanned tissues. Many RF coils (antennas) 12 and 14 are designed to have uniform RF sensitivity or gain throughout their working volume. Although images derived from such coils appear visually uniform, there are often significant departures from the ideal that disturb intensity-based segmentation methods. One arena where this is often apparent is differentiating white matter and gray matter in the brain. In the ideal case these tissue types give rise to distributions of intensities that are easily distinguished. In practice, the spatial intensity inhomogeneities are often of sufficient magnitude to cause the distributions of intensities associated with these two classes to overlap significantly. In addition, details of the operating conditions of the equipment have a significant effect on the observed intensities, often necessitating manual training on a per-scan basis. Therefore, a reliable method is needed to correct and segment MR images.